Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

Agri-food supply chains have received the attention of many researchers in recent years for various reasons, including food security and health-related issues. Wheat, as a staple food in many countries, is the most cultivated crop in the world. Due to the importance of wheat, this paper proposes a mixed-integer linear mathematical model for redesigning and planning of the wheat supply chain. The proposed model determines the location and capacity of new storage facilities while addressing supplier selection, ordering, storing, transportation, and distribution problems. This model considers the differences between long-term and short-term storage facilities and the quality of wheat. Moreover, the proposed model addresses the uncertainties associated with the quantity of domestic supply and demand through a stochastic scenario-based programming approach. Applicability of this model is investigated using real data from the wheat supply chain of Iran. Results show that seven new long-term storage facilities should be opened, which decreases total costs by 3.45 percent.

Recommender systems help users faced with the problem of information overflow and provide personalized recommendations. Social networks are used for providing variety of business or social activities, or sometimes a combination of both. In this paper, by considering social network of users and according to users’ context and items, a new method is introduced that is based on trust and context aware for recommender systems in social networks. The purpose of this paper is to create a recommender system which increases precision of predicted ratings for all users especially for cold start users. In the proposed method, walking on web of trust is done by neighbor users for finding rating of similar items and users’ preference is gotten of items’ context. The results show that suitable recommendation with user’s context is provided by using this method. Also, this system can increase precision of predicted rating for all users and cold starts too and however, do not decrease the rating’s coverage.

This paper investigates credit allocation policy making and its effect on economic development using bi-level programming. There are two challenging problems in bi-level credit allocation; at the first level government/public related institutes must allocate the credit strategically concerning sustainable development to regions and industrial sectors. At the second level, there are agent banks, which should allocate the credit tactically to individual applicants based on their own profitability and risk using their credit scoring models. There is a conflict of interest between these two stakeholders but the cooperation is inevitable. In this paper, a new bi-level programming formulation of the leader-follower game in association with sustainable development theory in the first level and data mining classifier at the second level is used to mathematically model the problem. The model is applied to a national development fund (NDF) as a government related organization and one of its agent banks. A new algorithm called Bi-level Genetic fuzzy apriori Algorithm (BGFAA) is introduced to solve the bilateral model. Experimental results are presented and compared with a unilateral policy making scenario by the leader. Findings show that although the objective functions of the leader are worse in the bilateral scenario but agent banks collaboration is attracted and guaranteed.

Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.

One of the primary issues on marketing planning is to know the customer & apos; s behavioral trends. A
customer & apos; s purchasing interest may fluctuate for different reasons and it is important to find the
declining or increasing trends whenever they happen. It is important to study these fluctuations
to improve customer relationships. There are different methods to increase the customer & apos; s
willingness such as planning good promotions, an increase on advertisement, etc. This paper
proposes a new methodology to measure customer & apos; s behavioral trends called customer
electrocardiogram. The proposed model of this paper uses K-means clustering method with
RFM analysis to study customer & apos; s fluctuations over different time frames. We also apply the
proposed electrocardiogram methodology for a real-world case study of food industry and the
results are discussed in details